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Mathematical Problems in Engineering
Volume 2017, Article ID 1376726, 9 pages
https://doi.org/10.1155/2017/1376726
Research Article

Feature Extraction and Fusion Using Deep Convolutional Neural Networks for Face Detection

College of Sciences, Northeastern University, Shenyang 110819, China

Correspondence should be addressed to Xiangde Zhang; nc.ude.uen.liam@edgnaixgnahz

Received 12 August 2016; Revised 17 October 2016; Accepted 26 October 2016; Published 24 January 2017

Academic Editor: Wonjun Kim

Copyright © 2017 Xiaojun Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper proposes a method that uses feature fusion to represent images better for face detection after feature extraction by deep convolutional neural network (DCNN). First, with Clarifai net and VGG Net-D (16 layers), we learn features from data, respectively; then we fuse features extracted from the two nets. To obtain more compact feature representation and mitigate computation complexity, we reduce the dimension of the fused features by PCA. Finally, we conduct face classification by SVM classifier for binary classification. In particular, we exploit offset max-pooling to extract features with sliding window densely, which leads to better matches of faces and detection windows; thus the detection result is more accurate. Experimental results show that our method can detect faces with severe occlusion and large variations in pose and scale. In particular, our method achieves 89.24% recall rate on FDDB and 97.19% average precision on AFW.